Improved Margin Generalization Bounds for Voting Classifiers
Machine Learning
2025-06-04 v2 Data Structures and Algorithms
Statistics Theory
Machine Learning
Statistics Theory
Abstract
In this paper we establish a new margin-based generalization bound for voting classifiers, refining existing results and yielding tighter generalization guarantees for widely used boosting algorithms such as AdaBoost (Freund and Schapire, 1997). Furthermore, the new margin-based generalization bound enables the derivation of an optimal weak-to-strong learner: a Majority-of-3 large-margin classifiers with an expected error matching the theoretical lower bound. This result provides a more natural alternative to the Majority-of-5 algorithm by (H{\o}gsgaard et al., 2024), and matches the Majority-of-3 result by (Aden-Ali et al., 2024) for the realizable prediction model.
Cite
@article{arxiv.2502.16462,
title = {Improved Margin Generalization Bounds for Voting Classifiers},
author = {Mikael Møller Høgsgaard and Kasper Green Larsen},
journal= {arXiv preprint arXiv:2502.16462},
year = {2025}
}